There exist several modifications and extensions of WBS.
[1]} consider WBS with the lengths of the background intervals in \(\mathcal {R}_n\) 
bounded from above by \(\delta _n\) ,
which ensures that each background interval contains a single change point at most,
leading to an improvement of the localisation rate.
[2]} propose a change point-like feature detection method termed
the narrowest-over-threshold (NOT) where, at each recursive iteration,
the shortest interval \((s, e]\) 
over which the associated CUSUM test statistic, \(\max _{s < k < e} \vert \mathcal {T}_{s, k, e}(X) \vert \) ,
exceeds a given threshold is selected, rather than
the interval returning the largest CUSUM.
[3]} proposes WBS2, which iteratively draws random intervals
and produces a complete solution path,
i.e. an ordered sequence of \(\mathcal {I}_1 = \lbrace 1, \ldots , n - 1\rbrace \) 
according to their importance measured by the associated CUSUM statistics.
[4]} investigate the seeded binary segmentation (SBS),
which generates the search grid \(\mathcal {R}_n\)  in a deterministic fashion.
In [5]}, a sampling strategy is investigated in combination with BS and WBS
for handling very large \(n\) .
